How to design a study that uses SPSS? - BulletPoints 1

• Three important situations in which you can use SPSS: (1) checking the reliability of a sample; (2) checking the reliability of results; (3) visualize data.

• The reliability of a scale indicates to what extent the scale is free from random error. There are two types of reliability: test-retest reliability and internal consistency.

• The reliability of a scale indicates to what extent the scale is free from random error. There are two types of reliability: content validity, criterion validity, and construct validity.

How to make a codebook for SPSS? - BulletPoints 2

• Before you can enter all information from questionnaires and experiments in IBM SPSS it is necessary to make a codebook. This is a summary of the instructions that you will use to convert the information of each test subject into a format that IBM SPSS can understand. Preparing a codebook consists of (1) defining and labeling each variable, and (2) assigning numbers to all possible answers.

• A codebook basically consists of four columns:
  (1) The abbreviated name of the variable (for example 'ID' for 'identification number);
  (2) The written name of the variable (for example 'identification number');
  (3) An explanation of how the possible answers are taught (for example 1 = men, 2 = women);
  (4) The measurement scale (for example nominal).

How to start with IBM SPSS? - BulletPoints 3

• It is important to always save your data when you are working with it. Saving does not happen automatically in IBM SPSS. To save a file, go to the File menu, then choose Save. You can also click on the icon that looks like a floppy disk. You can see this at the top left of your screen. Always ensure that your file is saved on your computer and not on an external drive. When you save the file for the first time, you must create a name for the file and choose a folder where you want to save the file. IBM SPSS automatically ensures that your file is saved with .sav at the end.

• It is important to know that an output file can only be opened in IBM SPSS. If you send your file to someone else who does not have the IBM SPSS program, he or she cannot open your file. To remedy this, you can export your output. Select File and then Export. You can now choose the type, for example PDF or Word. Then choose the Browse button to create a folder in which you want to save the file and choose a suitable name in the Save File line. Then click Save and OK.

How to create a file and enter your data in SPSS? - BulletPoints 4

• There are four steps in determining variables: (1) Create variables; (2) Assign labels to the answer categories and the missing values; (3) Entering data; (4) Clean up data.

• There are two ways to create a new variable. In the first way, a new variable is created by entering new data. In the second way, a variable is created that is based on existing data in the data set. For example, two variables are then combined to create a new, third variable.

• For some analyzes you only need a part of your sample. For example: only the men. You must then select this group in SPSS. You do this by using the Select Cases option. When you have selected the group of men, all women are deleted in SPSS. All analyzes that you will subsequently do will only be done for men.

How to screen and clean up data in SPSS? - BulletPoints 5

• Before you can analyze your data it is important to check your data file for errors, possible errors. First, it is important to see if you have made typos (see above). In addition, it is essential to investigate whether there are other errors with your data. For this you follow the following steps. Step 1: Checking for errors. First it is necessary to check all scores of all variables. You then investigate whether there are certain scores that fall outside the normal range. Step 2: Finding and checking error in the data file. It is then necessary to find out where the error is in the data file. This error must then be corrected or removed.

How to use SPSS for descriptive statistics? - BulletPoints 6

• When you are sure that there is no error in your data file, you can start with the descriptive phase of your data analysis. We called this descriptive statistics. These have as purpose: (1) to describe the characteristics of your sample in the method section of your article; (2) to check your variables to investigate whether you meet certain assumptions associated with the statistical techniques you want to implement to answer your research questions; (3) to ask specific research questions.

• The Skewness function provides information about the symmetry of the distribution of the scores. Kurtosis provides information about the peak of distribution. If the distribution of the scores were perfectly normal, both the skewness and the kurtosis would be zero. A positive value of skewness indicates that the scores are mainly on the left. Negative values ​​suggest that the scores are on the right side of the mean. A kurtosis of almost zero indicates a distribution that relationships are flat (too many test subjects in the extreme scores).

• When conducting research, in particular on people, you rarely get all the information from every case. That is why it is important that the research also looks at the missing data. This is possible in SPSS using the Missing Value Analysis procedure (bottom option in the Analyze menu). You must also decide how to deal with missing data when performing statistical analyzes. The Options button in many of the statistical procedures in SPSS offers various options regarding dealing with missing data. It is important that you choose carefully, since it can have major consequences for your results.

Which graphs can you use to display data? - BulletPoints 7

• In SPSS there are different types of graphs and charts that you can use to display data. The views discussed in this chapter are histograms, bar charts, line charts, scatter charts, and boxplots.

How to manipulate data in SPSS? - BulletPoints 8

• If the raw data has been accurately entered into SPSS, the next step is to edit and prepare the data so that later analyzes can be performed and hypotheses can be tested.

• Make sure that you also adjust the codebook for everything you adjust. An alternative is to use the Syntax option, this means that you keep track of all actions to be performed in the Syntax Editor, so that there is a list of what has been adjusted.

How to check the reliability of a scale? - BulletPoints 9

• The value of a study largely depends on the reliability of the scale used. One aspect of reliability is internal consistency: the degree to which the items of a scale associated with each other. This can for example be calculated with the Cronbach's cofficient alpha in SPSS. A Cronbach's alpha of .7 or greater indicates a reliable scale. However, with short scales with few units, there are low Cronbach values ​​and they don't say much.

Which method to use in SPSS? - BulletPoints 10

• Some studies use a single method, but many studies use multiple methods. In any case, it is crucial to choose the right research method. In this chapter, several methods are discussed: chi-square, correlation, partial correlation, multiple regression analysis, independent t-test, paired t-test and various forms of analysis of (co-)variance; oneway- and two-way (M)AN(C)OVA.

When and how is a correlation analysis applied? - BulletPoints 11

• Correlation analysis is applied to indicate the strength and direction of a linear relationship between two variables. Two correlation coefficients are mentioned in this chapter: (1) Pearson r for continuous variables (at interval level) and in cases where there is one continuous and one dichotomous variable, and (2) Spearman rho for variables at ordinal level and in cases that your data does not meet the criteria for the Pearson correlation. This text shows how to calculate a bivariate Pearson r and a non-parametric Spearman rho.

• To interpret the values ​​you can best use the Cohen guidelines:
  Small: r = .10 to .29 (or -.10 to -.29)
  Average: r = .30 to .49 (or -.30 to -.49)
  Large: r = .50 to 1.0 (or -.50 to -1.0)

What is the difference between correlation and partial correlation? - BulletPoints 12

• The partial correlation is similar to Pearson r, with the difference that with the partial correlation you can check for an additional (confound) variable.

How to perform multiple regression in SPSS? - BulletPoints 13

• Multiple regression is not just one technique, but a collection of techniques that can be used to study the relationship between a continuous dependent variable and multiple independent variables or predictors (usually continuous). It is based on correlation, but offers a more refined analysis of the relationship between a series of variables. Multiple regression can be applied to various research questions.

• In the standard multiple regression, all independent (or predictive) variables are compared simultaneously.

• In the hierarchical multiple regression (also called sequential regression), the independent variables are added to the equation in the order determined by the researcher on the basis of a theoretical framework. Variables or sets of variables are added in steps. Each variable is measured in terms of what it adds to the prediction of the dependent variable after checking for the other variables. 

• In step-by-step regression, the researcher provides a list of independent variables and then lets the program select, based on a set of statistical criteria, which variables are added and in which order they are added to the comparison. There are three different versions of this approach: (1) forward selection, (2) backward deletion, and (3) step-by-step regression.

How to perform a logistic regression analysis in SPSS? - BulletPoints 14

• Using logistic regression you can test models with which you can predict categorical outcomes - consisting of two or more categories. Using logistic regression you can measure how well your set of predictive variables is able to predict or explain your categorically dependent variable. It offers you an indication of the adequacy of your model by mapping the 'goodness of fit'. Your independent variable can be either categorical or continuous, or a combination of both.

• For logistic regression, assumptions are made regarding the sample size, multicollinearity, and outliers. 

How to perform factor analysis in SPSS? - BulletPoints 15

• Factor analysis differs from many of the other techniques in SPSS. It is not designed to test hypotheses or to indicate whether one group differs significantly from the other. Instead it takes a large set of variables and looks for a way to 'reduce' or summarize the data by using a smaller set of factors or components. This is done by searching for clusters or groups between the intercorrelations of a set of variables. There are two core approaches to factor analysis: (1) explorative factor analysis - often used during the early stages of research to collect information about the relationships between a set of variables - and (2) confirmatory factor analysis - applied later in the research process to specific hypotheses or theories regarding test the underlying structure of a set of variables.

• There are two important issues that you should take into account when determining the suitability of your dataset for factor analysis: sample size and the strength of the relationship between your variables (or items). There are not really clear guidelines for the sample size. Generally applies; the bigger the better. If you have a small sample (

• The second issue concerns the strength of the intercorrelations between the items. Tabachnick and Fidell recommend that correlation coefficients have values ​​greater than .3. SPSS offers two statistical measurements that can help determine the factorability of the data: (1) Bartlett's test for sphericity, and (2) Kaiser-Meyer-Olkin (SME) measurement for sample adequacy. Bartlett's test must be significant (p <.05 for="" appropriate="" factor="" analysis.="" the="" sme="" index="" must="" have="" a="" minimum="" value="" of="" .6="" good="">

How to use SPSS for non-parametric statistics? - BulletPoints 16

• Non-parametric statistics are ideal when your data is measured on a nominal or ordinal scale. They are also useful when you have very small samples and when your data does not meet the assumptions of the parametric techniques.

• General assumptions of non-parametric techniques that require checking are: (1) Random samples; (2) Independent observations (with the exception of techniques where repeated measurements are performed). In addition, some techniques have additional assumptions.

• The Chi-square test for independence is used when you want to study the relationship between two categorical variables. Each of these variables can have two or more categories. The chi-square test for independence compares the observed frequencies or proportions of cases that occur in each of the categories with the values ​​that are expected if there is no association between the measured variables. When SPSS encounters a 2x2 table (2 categories in each variable), the output includes an additional correction value (Yates' Correction for Continuity); this value is designed to compensate for what some researchers regard as an overestimate of the chi-square value when it is used in a 2x2 table.

• Kappa's measure of agreement (measure of agreement) is often applied when the inter-assessor reliability must be established. Kappa is an estimate of the degree of agreement between two assessors or tests. This takes into account the degree of agreement that might have happened by chance (coincidence).

• Sensitivity indicates the proportion of cases with a disease or disorder that have been correctly diagnosed. Specificity indicates the proportion of cases without the disease or disorder that have been correctly classified. 

Which t-tests can be used in SPSS? - BulletPoints 17

• There are different t-tests available in SPSS, the following two will be discussed here: T-test for independent samples (independent-samples t-test): this test is used to compare the means of two different groups of people or conditions. T-test for paired samples (paired-samples t-test): this test is used to compare the means of the same group of people at two different times or when there are equal (matched) pairs.

• The effect size can be determined with the Cohen guidelines:
  0.01 is a small effect
  0.06 is an average / moderate effect
  0.14 is a big effect.

How to use one-way ANOVA in SPSS? - BulletPoints 18

• In this chapter we discussed two types of one-way ANOVA's, namely: Between-groups ANOVA, which is used when dealing with different participants / cases in each of your groups (also called the independent groups design). Repeated measures ANOVA, which is used when you compare the same participants under different conditions / times (also called the within-subjects design).

• The one-way between-groups ANOVA is applied when you have one categorically independent (grouping) variable with at least three levels (groups) and one continuously dependent variable.

• In a one-way repeated measures ANOVA design, each participant is exposed to two or more conditions or measured on the same continuous scale at three or more times. The technique can also be used to compare participants' responses to two or more different questions or items. It is important here that the questions must be measured on the same scale (eg 1 = completely disagree, up to 5 = completely agree).

• Use planned comparisons when you are interested in comparisons between specific groups. This technique is more sensitive in detecting differences. Post-hoc testing, on the other hand, sets stricter significance levels to reduce the risk of Type 1 errors. You must decide whether you use post-hoc tests or planned comparisons before you begin your analysis.

How to use two-way ANOVA in SPSS? - BulletPoints 19

• Two-way means that there are two independent variables. Between-groups indicates that there are different participants in each of the groups. The two-way between-groups ANOVA can be used to look at the individual and collective influence of two independent variables on one dependent variable. You can therefore not only test the main effect for each independent variable, but also see whether there is possibly an interaction effect. The latter effect occurs when the influence of an independent variable on the dependent variable is dependent on a second independent variable.
   
• The most important output of the two-way ANOVA can be found in the table called Tests of Between-Subjects. The first thing you do is see if there is an interaction effect. Namely, when this is the case, it becomes more difficult to interpret the main effects.
   
• If you find a significant interaction effect, it is advisable to perform follow-up tests to examine this relationship more precisely (only if one of your variables consists of at least three levels). This can, for example, be done on the basis of a simple effect analysis. This means that you will view the results of each of the subgroups separately. To do this, you must split the sample into groups according to one of your independent variables and perform separate one-way ANOVAs to investigate the effect of the other variable.